针对水下建筑物裂缝检测问题,研发了一款新型智能水下机器人,此机器人具备恒温控制、低耗能驱动功能,可以在超低温深水环境下进行自主采集数据、导航与定位。基于机器人采集得到的图像数据,在图像预处理、深度卷积网络理论和裂缝特征数据标注的基础上,改进了原始的CNN模型,提出了特征金字塔融合卷积神经网络模型FPECNN,对不同类型的裂缝进行了提取。将FPECNN网络应用于莲花水电站大坝的裂缝检测工程中,计算结果表明FPECNN在检测率、召回率和F值上都处于较高的水平,达到了97.26%、98.04%和96.65%,耗时为3.12 s;FPECNN网络普适性与鲁棒性更佳,能够适应大多数的裂缝数据,生存能力更好,有利于解决常规CNN模型在水下建筑物检测中检测率低、效率低的问题。该智能机器人可将检测人员从高寒水下恶劣、繁重和危险的现场作业中解脱出来,同时解决水电站传统检测中因弃水造成的巨大经济损失问题,并能提高检测效率和精度。

An intelligent underwater robot was developed for crack detection of underwater buildings. The robot has constant temperature control and low energy consumption drive, and has functions of data collection, navigation, and positioning independently in ultra-low temperature deepwater environment. Based on the image data collected by the robot, the original CNN (Convolutional Neural Network) model was improved on the basis of image preprocessing, deep convolution network theory and fracture feature data annotation. Hence, an FPECNN (Feature Pyramid Engagement Convolution Neural Network) model was proposed to extract different types of cracks. The FPECNN model was applied to the crack detection project of Lianhua Hydropower Station. The calculation results demonstrate that the FPECNN model stands at a high level in detection rate, recall rate and F value, reaching 97.26, 98.04 and 96.65, respectively, consuming only 3.12 s. It is also well universal, robust, and viable as it adapts to most crack data, and refrains from low detection rate and low efficiency of conventional CNN model in the detection of underwater buildings. With this intelligent robot, the inspection personnel can be relived from the harsh, heavy and dangerous field work in cold underwater, and the huge economic loss caused by the abandonment of water in traditional inspections can be avoided, and improve the detection efficiency and accuracy.